R Intermediate - Day 2

---

R DATA VISUALIZATION

Why Data Visualization?

Raw data point doesn’t provide much insight to kick off data analysis.

Data Visualization is brilliant in * exploring the pattern of data briefly at the early stage * in the final conclusion, enhance the story telling of data analysis (inforgraphics play a huge role for this purpose)

Reminder on Workflow Again

Like the picture above shows, in the stage of Understand (exploring the data sets), Transform, Visualize and Model are used in an iterative manner so as to get the best early understaning about the data sets.

Built-in Plot Functions

The advantage of using built-in plotting utilities is they are easy. It let you quickly visualize the data pattern while you are trying to gain a brief insight at the early stage of your workflow.

Reflash on Built-in Plot Tools

For built-in R data visualization, go to the R Programming Intro project on Github to reflash your memory (R Intro Source Codes)[https://github.com/ngsanluk/R-Intro]

Grammar of Graphics: ggplot2

If these built-in plotting tools are not enough for you,  Go to ggplot2. It is the most popular data visualization for R.

ggplot2 is an open-source data visualization package for R. A data visualization which breaks up graphs into semantic components such as scales and layers.  Since 2005, ggplot2 has grown in use to become one of the most popular R packages.

ggplot2 Cheat Sheet

ggplot 2 cheat sheet

---

INSTALL / LOAD PACKAGES

if (!require("pacman")) install.packages("pacman") # check if pacman already installed. If not, install it.
pacman::p_load(
  pacman, # package manager
  datasets, # built-in data sets
  rio, # r input / output
  magrittr, # for piping commands
  tidyverse,
  modelr
) # Load required packages.  If they are NOT already downloaded, download them automatically.

BASIC GRAMMAR

ggplot2 is based on the grammar of graphics, the idea that you can build every graph from the same components below:

a data set + a coordinate system + and geoms—visual marks that represent data points

Grammar of Graphics

Use Built-in Datasets

Let’s use the built-in cars data set for a simple ggplot

geom_point() function

It’s easy to add geometry layer to the base co-ordinate
Let’s ADD a layer of data points using geom_point() function.

And Yes, you can ADD a layer by using the + operator We use geom_point() function that required x and y value for each point. In 2D co-ordinate, a point is described by its x and y value.

We need to provide a mapping that specifies the data columns’ name to map to a point’s the x and y value

That mapping is defined by an aesthetics function
aes()

Scatter plot is useful to explore the relation of two variables.

Use geom_line() to replace geom_point()

geom_point() and geom_line() require very similar parameters.
geom_line() is simply an enhanced visualization that automatically connect all the points

Use geom_smoth() to project a smooth line

again geom_smooth() and geom_point() require very similar parameters.
geom_smooth() smooths out the line progression

cars %>% ggplot() +
  geom_smooth(mapping = aes(x=speed, y=dist))

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Geometric Objects Comparison

a geom is the geometrical object that a plot use to represent data. We used points, lines and smooth above on the same data set and they provide very different message.

Adding Multiple Layers

# just change geom_point to geom_line without change anything else
cars %>% ggplot() +
  geom_point(mapping = aes(x=speed, y=dist)) +
  geom_smooth(mapping = aes(x=speed, y=dist)) 

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Adding Aesthetics to your Plots

Un-comment the extra parameters to add more aesthetics to your plot

cars %>% ggplot() +
  geom_point(mapping = aes(x=speed, y=dist),
             color = "orange", # the color of data points
             # size = 3, # the size of data point
             # alpha = 0.5, # the transparency of data points, min=0, max=1
             # shape = 0, # the shape of data point
             )

---

PLOT WITH OUR OWN DATA

Loading Data: allowance

Allowance Data Set in Simple Scatterplot

Continuous Values vs. Discrete Values

Continuous values refer to numbers value that has wide range. E.g. salary, height Discrete values refer to a limited number of valid values. It can be string. It can be a few distinct numbers.

When you produce plots, pay attention to what type of value are required by the geoms.

In many cases, you will need to convert the data first. mutate() function are quite often used for that.

example:

allowance = allowance %>% 
  mutate(Assessment_Year = as.numeric(substr(Assessment_Year, 1 ,4))) 

Simple Line Plot

Adding Multiple Layers of Geometry

allowance %>% ggplot() +
  geom_line(mapping = aes(x=Assessment_Year, y=Basic, group=1, color="Orange")) +
  geom_point(mapping = aes(x=Assessment_Year, y=Basic, group=1, color="Orange"))

# As both geom use the same data mapping, the above statements can be simplified as
allowance %>% ggplot(aes(x=Assessment_Year, y=Basic, group=1, color="Orange")) +
  geom_line() +
  geom_point(size=5)

Use geom_smooth() to smooth out the line

allowance %>% ggplot(aes(x=Assessment_Year, y=Basic, group=1, color="Orange")) +
  geom_smooth() +
  geom_point(size=5)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Save a Plot: ggsave()

ggsave("./output/my_first_plot.png") # default image size

Saving 7 x 7 in image

Bar Chart with geom_col()

geom_bar()

geom_bar() is used for counting the frequency of each occurrence of observed value. It’s usually for counting a limit set of value

---

CHALLENGE

Multiple Layers of Lines

add line plot for column of Child in the same plot, add another line plot for Dependent_Parent_60

allowance %>% ggplot() +
  geom_line(mapping = aes(x=Assessment_Year, y=Child, group=1), color="Orange") + 
  geom_line(mapping = aes(x=Assessment_Year, y=Dependent_Parent_60, group=1), color="Blue") 

---

WORK WITH MORE COMPLEX DATA

Loading Data: graduates.csv

graduates = read_csv("./data/graduates.csv")

Rows: 601 Columns: 5
── Column specification ─────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): AcademicYear, LevelOfStudy, ProgrammeCategory, Sex
dbl (1): Headcount

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Simple Scatterplot

Let’s explore the data with some simple ggplot plot. Overall they are not very useful. Just some quick exploration.

Use filter() to Extract Required Rows

Use Line Plot To Explore the Trending

Use line plot to explore the trending of “Business and Management” student headcount trending

---

CHALLENGE

Comparison with Line Plots

Use line plots to compare female undergraduate students headcount trending in ProgrammeCategory of “Business and Management” and “Engineering and Technology” Use filter() to extract required record You can use multiple filter() call Use &, | or multiple conditions

graduates %>% 
  .$ProgrammeCategory %>% 
  unique() # display the unique names of ProgrammeCategory

[1] "Arts and Humanities"            "Business and Management"       
[3] "Education"                      "Engineering and Technology"    
[5] "Medicine, Dentistry and Health" "Sciences"                      
[7] "Social Sciences"               

graduates %>% 
  filter(LevelOfStudy=="Undergraduate", Sex=="F") %>%
  filter(ProgrammeCategory=="Business and Management" | ProgrammeCategory=="Engineering and Technology") %>% 
  print() # Test extracting and printing the required records.

---

CHALLENGE: line plot for hibor_fixing_1m

library(jsonlite) # load package
hkma.interbank.url = "https://api.hkma.gov.hk/public/market-data-and-statistics/daily-monetary-statistics/daily-figures-interbank-liquidity"
interbank.liquidity = fromJSON(hkma.interbank.url)
# the above retrieval will take a while.  The server response is slow.
summary(interbank.liquidity)

       Length Class  Mode
header 3      -none- list
result 2      -none- list

str(interbank.liquidity)

List of 2
 $ header:List of 3
  ..$ success : logi TRUE
  ..$ err_code: chr "0000"
  ..$ err_msg : chr "No error found"
 $ result:List of 2
  ..$ datasize: int 100
  ..$ records :'data.frame':    100 obs. of  44 variables:
  .. ..$ end_of_date                                    : chr [1:100] "2022-03-30" "2022-03-29" "2022-03-28" "2022-03-25" ...
  .. ..$ cu_weakside                                    : num [1:100] 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 ...
  .. ..$ cu_strongside                                  : num [1:100] 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 ...
  .. ..$ disc_win_base_rate                             : num [1:100] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
  .. ..$ hibor_overnight                                : num [1:100] 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.03 0.02 ...
  .. ..$ hibor_fixing_1m                                : num [1:100] 0.313 0.312 0.313 0.323 0.319 ...
  .. ..$ twi                                            : num [1:100] 95.3 95.7 95.9 95.6 95.7 95.7 95.6 95.5 95.4 95.4 ...
  .. ..$ opening_balance                                : int [1:100] 337534 337534 337534 337534 337534 337536 337553 337538 337538 337538 ...
  .. ..$ closing_balance                                : int [1:100] 337551 337534 337534 337534 337534 337534 337536 337553 337538 337538 ...
  .. ..$ market_activities                              : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ interest_payment                               : chr [1:100] "+16" "+0" "+0" "+0" ...
  .. ..$ discount_window_reversal                       : chr [1:100] "-0" "-0" "-0" "-0" ...
  .. ..$ discount_window_activities                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ intraday_movements_of_aggregate_balance_at_0930: int [1:100] 363880 350696 357104 354934 363276 355552 353301 352138 351573 353926 ...
  .. ..$ intraday_movements_of_aggregate_balance_at_1000: int [1:100] 366723 358279 358428 358128 363476 361781 356632 356870 353300 355233 ...
  .. ..$ intraday_movements_of_aggregate_balance_at_1100: int [1:100] 383598 376635 375870 374775 374465 377616 374645 370373 371865 373477 ...
  .. ..$ intraday_movements_of_aggregate_balance_at_1200: int [1:100] 331056 383952 385132 383777 383416 323375 375169 381656 378485 395569 ...
  .. ..$ intraday_movements_of_aggregate_balance_at_1500: int [1:100] 334817 391180 389282 382202 390063 335473 391970 403349 403099 398017 ...
  .. ..$ intraday_movements_of_aggregate_balance_at_1600: int [1:100] 337197 392468 391754 383194 391833 337396 392436 403610 409198 401337 ...
  .. ..$ forex_trans_t1                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ other_market_activities_t1                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ reversal_of_discount_window_t1                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  .. ..$ interest_payment_issuance_efbn_t1              : chr [1:100] "+0" "+16" "+0" "+0" ...
  .. ..$ forecast_aggregate_bal_t1                      : int [1:100] 337551 337551 337534 337534 337534 337534 337534 337536 337536 337538 ...
  .. ..$ forex_trans_t2                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ other_market_activities_t2                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ reversal_of_discount_window_t2                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  .. ..$ interest_payment_issuance_efbn_t2              : chr [1:100] "+0" "+0" "+7" "+0" ...
  .. ..$ forecast_aggregate_bal_t2                      : int [1:100] 337551 337551 337541 337534 337534 337534 337534 337529 337536 337536 ...
  .. ..$ forex_trans_t3                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ other_market_activities_t3                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ reversal_of_discount_window_t3                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  .. ..$ interest_payment_issuance_efbn_t3              : chr [1:100] "+0" "+0" "+0" "+7" ...
  .. ..$ forecast_aggregate_bal_t3                      : int [1:100] 337551 337551 337541 337541 337534 337534 337534 337529 337529 337536 ...
  .. ..$ forex_trans_t4                                 : chr [1:100] NA NA NA NA ...
  .. ..$ other_market_activities_t4                     : chr [1:100] NA NA NA NA ...
  .. ..$ reversal_of_discount_window_t4                 : chr [1:100] NA NA NA NA ...
  .. ..$ interest_payment_issuance_efbn_t4              : chr [1:100] NA NA NA NA ...
  .. ..$ forecast_aggregate_bal_t4                      : int [1:100] NA NA NA NA NA NA NA NA NA NA ...
  .. ..$ forex_trans_u                                  : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ other_market_activities_u                      : chr [1:100] "+0" "+0" "+0" "+0" ...
  .. ..$ reversal_of_discount_window_u                  : chr [1:100] "-0" "-0" "-0" "-0" ...
  .. ..$ interest_payment_issuance_efbn_u               : chr [1:100] "-72" "-72" "-62" "-62" ...
  .. ..$ forecast_aggregate_bal_u                       : int [1:100] 337479 337479 337479 337479 337479 337479 337479 337479 337479 337479 ...

interbank.liquidity$result

$datasize
[1] 100

$records

str(interbank.liquidity$result)

List of 2
 $ datasize: int 100
 $ records :'data.frame':   100 obs. of  44 variables:
  ..$ end_of_date                                    : chr [1:100] "2022-03-30" "2022-03-29" "2022-03-28" "2022-03-25" ...
  ..$ cu_weakside                                    : num [1:100] 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 7.85 ...
  ..$ cu_strongside                                  : num [1:100] 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 7.75 ...
  ..$ disc_win_base_rate                             : num [1:100] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
  ..$ hibor_overnight                                : num [1:100] 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.03 0.02 ...
  ..$ hibor_fixing_1m                                : num [1:100] 0.313 0.312 0.313 0.323 0.319 ...
  ..$ twi                                            : num [1:100] 95.3 95.7 95.9 95.6 95.7 95.7 95.6 95.5 95.4 95.4 ...
  ..$ opening_balance                                : int [1:100] 337534 337534 337534 337534 337534 337536 337553 337538 337538 337538 ...
  ..$ closing_balance                                : int [1:100] 337551 337534 337534 337534 337534 337534 337536 337553 337538 337538 ...
  ..$ market_activities                              : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ interest_payment                               : chr [1:100] "+16" "+0" "+0" "+0" ...
  ..$ discount_window_reversal                       : chr [1:100] "-0" "-0" "-0" "-0" ...
  ..$ discount_window_activities                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ intraday_movements_of_aggregate_balance_at_0930: int [1:100] 363880 350696 357104 354934 363276 355552 353301 352138 351573 353926 ...
  ..$ intraday_movements_of_aggregate_balance_at_1000: int [1:100] 366723 358279 358428 358128 363476 361781 356632 356870 353300 355233 ...
  ..$ intraday_movements_of_aggregate_balance_at_1100: int [1:100] 383598 376635 375870 374775 374465 377616 374645 370373 371865 373477 ...
  ..$ intraday_movements_of_aggregate_balance_at_1200: int [1:100] 331056 383952 385132 383777 383416 323375 375169 381656 378485 395569 ...
  ..$ intraday_movements_of_aggregate_balance_at_1500: int [1:100] 334817 391180 389282 382202 390063 335473 391970 403349 403099 398017 ...
  ..$ intraday_movements_of_aggregate_balance_at_1600: int [1:100] 337197 392468 391754 383194 391833 337396 392436 403610 409198 401337 ...
  ..$ forex_trans_t1                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ other_market_activities_t1                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ reversal_of_discount_window_t1                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  ..$ interest_payment_issuance_efbn_t1              : chr [1:100] "+0" "+16" "+0" "+0" ...
  ..$ forecast_aggregate_bal_t1                      : int [1:100] 337551 337551 337534 337534 337534 337534 337534 337536 337536 337538 ...
  ..$ forex_trans_t2                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ other_market_activities_t2                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ reversal_of_discount_window_t2                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  ..$ interest_payment_issuance_efbn_t2              : chr [1:100] "+0" "+0" "+7" "+0" ...
  ..$ forecast_aggregate_bal_t2                      : int [1:100] 337551 337551 337541 337534 337534 337534 337534 337529 337536 337536 ...
  ..$ forex_trans_t3                                 : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ other_market_activities_t3                     : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ reversal_of_discount_window_t3                 : chr [1:100] "-0" "-0" "-0" "-0" ...
  ..$ interest_payment_issuance_efbn_t3              : chr [1:100] "+0" "+0" "+0" "+7" ...
  ..$ forecast_aggregate_bal_t3                      : int [1:100] 337551 337551 337541 337541 337534 337534 337534 337529 337529 337536 ...
  ..$ forex_trans_t4                                 : chr [1:100] NA NA NA NA ...
  ..$ other_market_activities_t4                     : chr [1:100] NA NA NA NA ...
  ..$ reversal_of_discount_window_t4                 : chr [1:100] NA NA NA NA ...
  ..$ interest_payment_issuance_efbn_t4              : chr [1:100] NA NA NA NA ...
  ..$ forecast_aggregate_bal_t4                      : int [1:100] NA NA NA NA NA NA NA NA NA NA ...
  ..$ forex_trans_u                                  : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ other_market_activities_u                      : chr [1:100] "+0" "+0" "+0" "+0" ...
  ..$ reversal_of_discount_window_u                  : chr [1:100] "-0" "-0" "-0" "-0" ...
  ..$ interest_payment_issuance_efbn_u               : chr [1:100] "-72" "-72" "-62" "-62" ...
  ..$ forecast_aggregate_bal_u                       : int [1:100] 337479 337479 337479 337479 337479 337479 337479 337479 337479 337479 ...

interbank.records = interbank.liquidity$result$records %>% as_tibble()
interbank.records

interbank.records %>% 
ggplot() +
  geom_line(
    mapping=aes(x=end_of_date, y=hibor_fixing_1m, group=1),
    color="orange"
             )

---

GROUPING AND AGGREGATION

Using group_by() and summarise()

graduates %>% group_by(AcademicYear, LevelOfStudy) %>% 
  summarise(TotalHeadcount = sum(Headcount)) %>% 
  ggplot(
    aes(x=AcademicYear, 
             y=TotalHeadcount,
             group=LevelOfStudy, 
             color=LevelOfStudy
             )
    ) +
    geom_line() +
    geom_point() 

`summarise()` has grouped output by 'AcademicYear'. You can override using the `.groups`
argument.

Use of filter()

Use filter() to keep only “Taught Postgraduate” Records

This plot is not very useful without previously applying filter() and group_by() and summarise()

filter() + group_by() + summarise()

Use filter() to extract required rows Use group_by() and summarise() to group and aggreate total headcout for both male and female

graduates %>% 
  filter(LevelOfStudy=="Taught Postgraduate") %>% 
  group_by(AcademicYear, ProgrammeCategory) %>% 
  summarise(TotalHeadcount = sum(Headcount)) %>% 
  ggplot() +
    geom_line(mapping=aes(x=AcademicYear,y=TotalHeadcount, group=ProgrammeCategory, color=ProgrammeCategory))

`summarise()` has grouped output by 'AcademicYear'. You can override using the `.groups`
argument.

graduates %>%
  filter(LevelOfStudy=="Undergraduate") %>%
  group_by(AcademicYear, ProgrammeCategory) %>%
  summarise(TotalHeadcount = sum(Headcount)) %>%
  ggplot() +
    geom_line(mapping=aes(x=AcademicYear,y=TotalHeadcount, group=ProgrammeCategory, color=ProgrammeCategory))

`summarise()` has grouped output by 'AcademicYear'. You can override using the `.groups`
argument.

NA

geom_col() function

More Aggregation Functions

Center: mean(), median() Spread: sd(), IQR(), mad() Range: min(), max(), quantile() Position: first(), last(), nth(), Count: n(), n_distinct() Logical: any(), all()

More information at summarise() function

geom_bar() function

bar chart give the counting frequency (number of record in the data set)

box plot

The boxplot compactly displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all “outlying” points individually.

facet_wrap()

facets are useful categorical variables. It split your plot into subplot (a.k.a facets) that each display one subset of the data.

facet_wrap() lets you work with ONE extra variable (besides x and y).
Each categorical value will used to produce to a sub plot. We use LevelOfStudy here. Since there are FOUR distinct values, you will see FOUR sub-plots

graduates %>% ggplot() +
  geom_point(mapping = aes(x=AcademicYear, y=Headcount)) +
  facet_wrap(~ LevelOfStudy) # You will see FOUR sub plots as there are FOUR distinct values for this variable.

facet_grid()

facet_grid() lets you work with TWO extra variables (besides x and y).
Each combination of these two varaibles’ value are used to produce to a sub plot.

You should SEE 28 sub plots as there are FOUR distinct values for LevelOfStudy and 7 distinct values for ProgrammeCategory.

graduates %>% ggplot() +
  geom_point(mapping = aes(x=AcademicYear, y=Headcount)) +
  facet_grid(LevelOfStudy ~ ProgrammeCategory)

---

MAKE IT PRETTY

Use of title, label, background color and themes

# in this example we save the plot to a variable name 'level.bar.plot' so that we can use it again and again.
level.bar.plot = graduates %>% 
filter(ProgrammeCategory=="Engineering and Technology") %>% 
ggplot() +
  geom_col(mapping=aes(x=AcademicYear, y=Headcount, fill=LevelOfStudy))

# To show the plot, just use print() function with the previous saved plot variable as parameter.
print(level.bar.plot)

Plot Background

element_rect() is a function to generated rectangle geometry element. You have to specify the fill parameter by color name or hex code code by string

Plot Background refers to the big area of everything relevant to the plot.

Panel Background

Panel background refers to the inner area of plot. Area for showing header, axis lable and legend are NOT included.

level.bar.plot # default style
level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_rect(fill="orange")) # styling the panel background

Remove Plot and Panel Background

In visual design, color is very powerful tool to guide users’ attention. But you have to use them carefully.

Too many colors will usually do the opposite - confuse the audience. Minimal design is the recent trend. Expecially true when many are using small device like mobile phone for day-to-day communication.

In this example, we are removing both plot and panel background to achieve a clean design. After all, background is the main dish. Very often background color causes distraction to graph.

Change Label for x/y Axis

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") # Label for X axis

Ratate the Labe Text

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") + # Label for X axis
  theme(axis.text.x = element_text(angle = 45))

Change Fill Colors

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") + # Label for X axis
  scale_fill_manual(values=c("purple", "orange", "blue", "tomato"))

Styling The Legends

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") + # Label for X axis
  theme(legend.position="top") +
  scale_fill_manual(values=c("purple", "orange", "blue", "tomato"),
                    guide = guide_legend(title="Level of Study", 
                                         label.position = "bottom")
                    )

NA

Add Title and Subtitle

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") + # Label for X axis
  theme(legend.position="top") +
  scale_fill_manual(values=c("purple", "orange", "blue", "tomato"),
                    guide = guide_legend(title="Level of Study", 
                                         label.position = "bottom")
                    ) + # move legend position to top and label position to bottom 
  ggtitle("Hong Kong Higher Education Student Headcount", subtitle="2009 - 2019")

Add Annotations Texts

Add extra texts/shape to enhance your visualization

level.bar.plot # default style

level.bar.plot +
  theme(panel.background = element_blank()) + # styling the panel background to none
  theme(plot.background = element_blank()) + # styling the plot background to none
  theme(panel.grid.major.y = element_line(color="grey")) + # styling the grid line for y-axis
  ylab("Number of Student") + # Label for Y axis
  xlab("Year") + # Label for X axis
  theme(legend.position="top") +
  scale_fill_manual(values=c("purple", "orange", "blue", "tomato"),
                    guide = guide_legend(title="Level of Study", 
                                         label.position = "bottom")
                    ) + # move legend position to top and label position to bottom 
  ggtitle("Hong Kong Higher Education Student Headcount", subtitle="2009 - 2019") + 
  annotate("text", label="Record\nHigh", x="2017/18", y=5300) # you can change value of x and y to set the text position

Adding Reference Lines

Using Themes

level.bar.plot # default style

level.bar.plot +
  theme_bw() # black and white theme

level.bar.plot +
  theme_minimal() # black and white theme

level.bar.plot +
  theme_dark() # black and white theme

More 3rd-party Themes

Install ggthemes package to unlock wider selections of themes.

if (!require("pacman")) install.packages("pacman") # check if pacman already installed. If not, install it.
pacman::p_load(ggthemes)

level.bar.plot # default style

level.bar.plot +
  theme_excel() + # Excel Theme
  ggtitle("Excel Theme")

  

level.bar.plot +
  theme_wsj() + # Wall Street Journal Theme
  ggtitle("Wall Street Journal Theme")

level.bar.plot +
  theme_economist() + # Economist Theme
  ggtitle("Economist Theme")

level.bar.plot +
  theme_fivethirtyeight() + # Five Thirty Eight
  ggtitle("Five Thirty Eight Theme")

---

MORE RESOURCES ON ggplot2

official website

browseURL("https://ggplot2.tidyverse.org/")

extensions gallery

browseURL("https://exts.ggplot2.tidyverse.org/gallery")

---

MODELS

Work Follow of Data Science

Data Science is combination of efforts and results of programming, mathematics and domain expertise. Among all, mathematics is the foundation of models. With models, data scientists make predictions; discover hidden patterns; and conclude insights.

Modeling is usually an iterative process among data transformation, data visualization, exploring with models and fitting.

What exactly is a Model?

Human are good in drawing conclusions and providing insight while are NOT good in directly facing large number of data attributes and huge volume of raw data.

Models Example

A model is mathematics expression that provides a simple low-dimensional summary of a data set so that we can draw conclusion and even providing insights. Models only provide approximation (NOT the exact truth).

Basic Concepts of Model

Let’s do some simple R coding to uncover the basic concept of model

if (!require("pacman")) install.packages("pacman") # install pacman
pacman::p_load(pacman, tidyverse, modelr, magrittr) # install (or load) required packages

Let’s use a simple built-in data set sim1 for exploring. In this simulation data you can strongly see the pattern with the help of simple scatter-plot.

Generating a Random Linear Model

Linear line and quadratic curve are widely used to explore the relation of two variables. Let’s take linear model as simple example to grab the essence of model.

A linear model is described as y = a1 + x * a2 x and y are the variable from dataset a1 and a2 are parameters that can vary to capture different patterns.

Let’s generate a random value of a1 as intercept and a2 as slope. Here, we use runinf() to generate a random uniform distributed number

Note: You might have to repeatively run a few times before you can see the visualized random model represented by a orange straight line.

Generating 250 Random Models as Candidate Models

The number of potential models are unlimited. Let’s try to generate 250 random ones as candidate models.
Among these 250 models, some are very bad that you can judge even by glancing. Some are not bad but we don’t know which one is the best among them.

models = tibble(
  a1 = runif(250, -20, 40), # 250 random intercept values between -20 to 40
  a2 = runif(250, -5, 5) # 250 random slop values between -5 to 5
)

ggplot(sim1, aes(x,y)) +
  geom_point() +
  geom_abline(aes(intercept = a1, slope = a2), data=models, alpha=0.2)

Selecting the Most Fitting Ten Models

Using lm() function

If the previous R codes on choosing best 10 among 250 random models are too much digest. It’s fine. It’s just for you to feel the process and essence of models and fitting models.

In fact, R makes linear model fitting extremely easy by just one single line of function calling to the lm() function (a built-in linear model fitting function)

lm() finds the closest model in a single step, using a sophisticated algorithm that involves geometry, calculus, and linear algebra.

lm() has a special way to specify the model family: formulas. Formulas look like y ~ x, which lm() will translate to a function like y = a1 + a2 * x

sim1_auto_model = sim1 %>%  lm(y ~ x, .) # finding the optimized linear model
sim1_auto_model = sim1 %>%  lm(y ~ x, .) # finding the optimized linear model
print(sim1_auto_model) # print out the auto generated linear model

Call:
lm(formula = y ~ x, data = .)

Coefficients:
(Intercept)            x  
      4.221        2.052  

Making Prediction

new.data = data.frame(x = c(1,2,3,4,5,6,7,8,9,10))
predict(sim1_auto_model, new.data)

        1         2         3         4         5         6         7         8 
 6.272355  8.323888 10.375421 12.426954 14.478487 16.530020 18.581553 20.633087 
        9        10 
22.684620 24.736153 

Using lm() function with categorical data

Using lm() function on a categorical variable will use mean value for each category for prediction.

Multiple Regression

Multiple regression is an extension of linear regression into relationship between more than two variables.

predict(m.r.model, new.data.2)

       1 
22.65987 

Logistic Regression

summary(bi.model)

Call:
glm(formula = am ~ cyl + hp + wt, family = binomial, data = .)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.17272  -0.14907  -0.01464   0.14116   1.27641  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept) 19.70288    8.11637   2.428   0.0152 *
cyl          0.48760    1.07162   0.455   0.6491  
hp           0.03259    0.01886   1.728   0.0840 .
wt          -9.14947    4.15332  -2.203   0.0276 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 43.2297  on 31  degrees of freedom
Residual deviance:  9.8415  on 28  degrees of freedom
AIC: 17.841

Number of Fisher Scoring iterations: 8

---

VISUALIZATION VS. MODEL

Visualization is usually thought as a tool for hypothesis generation to explore the hidden patterns among data while modelling is usually thought as at tool for hypothesis confirmation (to confirm what are found by data visualization tools). These two tools are suggested to used in an iterative manner so as to achieve a deeper revealing on data.

FURTHER LEARNING RESOURCES

More on ggplot2

ggplot2 tutorial

browseURL("https://www.tutorialspoint.com/ggplot2/ggplot2_introduction.htm")

ggplot2: Elegant Graphics for Data Analysis (Web-based E-book) ###

browseURL("https://ggplot2-book.org/")

R Statiscal Models

more on model building

A execellent ebook for R Data Science * Basic Concept of Model * Model Building * Example on Model A web based ebook at

browseURL("https://r4ds.had.co.nz/model-intro.html")

examples on more models

• Linear Regression
• Multiple Regression
• Logistic Regression
• Decision Tree
• Random Forest
• And so on

browseURL("https://www.tutorialspoint.com/r/r_linear_regression.htm")

R Studio Cheatsheets

Many quick syntax reference for R programming and 3-rd packages

browseURL("https://www.rstudio.com/resources/cheatsheets/")

Advanced R

Advanced R

browseURL("https://adv-r.hadley.nz/index.html")

TO DELETE

p_data(modelr)
heights